There are many possibilities to improve our approach. It has been shown in the speech research field that LSTM RNNs achieved the best acoustic modeling performance [1]}. However, this method requires a large training dataset in order prevent from over-fitting. Another possibility to improve our acoustic model is to go deeper with the current single-layer CNN architecture, but this will also require more training data. We plan to collect more jingju a cappella singing recordings and perform data augmentation to leverage the capability of the acoustic models. Furthermore, in order to take advantage of the melodic information existing in both audio and score datasets, we also plan to investigate methods which can fuse melodic, phonetic and duration information.
